TLP:一个动态网络中的时序链路预测算法  

TLP:A Temporal Link Prediction Algorithm in Dynamic Network

作　　者：符汉杰 熊赟[1,2,3] 朱扬勇 FU Hanjie;XIONG Yun;ZHU Yangyong(School of Computer Science,Fudan University,Shanghai 200433,China;Shanghai Key Laboratory of Data Science,Shanghai 200433,China;Shanghai Institute for Advanced Communication and Data Science,Shanghai 200433,China)

机构地区：[1]复旦大学计算机科学技术学院,上海200433 [2]上海市数据科学重点实验室,上海200433 [3]上海先进通信与数据科学研究院,上海200433

出　　处：《计算机工程》2020年第1期67-73,共7页Computer Engineering

基　　金：国家自然科学基金(U1636207,91546105);上海市科委发展基金(16JC1400801,17511105502);苏州市科技局民生科技示范工程项目(SS201712,SS201812)

摘　　要：链路预测是网络分析的一个重要应用,网络结构在真实场景中随时间发生演变,节点间会产生新的联系或者终止连接,从而导致网络结构变化以及节点中内在的偏向发生偏移。为提升链路预测能力,提出一种基于时序特征的动态网络节点表示的链路预测算法,即每一时刻的节点表示向量由历史的表示向量计算得到,以反映节点在向量空间中的变化规律,同时结合节点间的高阶邻近特性,生成具有鲁棒性的节点向量来维护网络结构。在真实数据集上的实验结果表明,与TNE、DHPE等算法相比,该算法在链路预测任务上的预测性能具有明显提升,适用于大规模的动态网络。Link prediction is an important application of network analysis.In real scenarios,the network structure evolves with time,so new connections or terminations occur between nodes,resulting in changes in the network structure and deviations within the nodes.In order to improve the link prediction capability,this paper proposes a link prediction algorithm based on node representation with temporal characteristics in dynamic network.The node representation vector at each moment is obtained by calculating the historical representation vector,so as to reflect the variation patterns of nodes in the vector space.Meanwhile,by combing the high order proximity characteristics between nodes,robust node vectors are generated to preserve the network structure.Experimental results on real datasets show that compared with algorithms such as TNE and DHPE,the propose algorithm presents a good performance improvement on link prediction tasks and it can be applied to large scale dynamic networks.

关 键 词：链路预测 动态网络 随机游走 时序演变 高阶邻近 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]